Humanized Conversation API (using LLM)

conversations in a human way without exposing that it's a LLM answering

For more information you can use our documentation

To use this project, you need to have a .csv file with the knowledge base and a .toml file with your prompt configuration.

We recommend that you create a folder inside this project called data and put CSVs and TOMLs files over there.

fields:

• category
• subcategory: used to customize the prompt for specific questions
• question
• content: used to generate the embedding

example:

category,subcategory,question,content
faq,promotions,loyalty-program,"The company XYZ has a loyalty program when you refer new customers you get a discount on your next purchase, ..."

To load the knowledge base into the database, make sure the database is up and then, inside src folder, run make load-data path="../data/know.csv" (or pass another path to you .csv).

The [prompt.header], [prompt.suggested], and [fallback.prompt] fields are mandatory fields used for processing the conversation and connecting to the LLM.

The [prompt.fallback] field is used when the LLM does not find a compatible embedding in the database; that is, the [prompt.header] is ignored and the [prompt.fallback] is used. Without it, there could be hallucinations about possible answers to questions outside the scope of the embeddings.

In [prompt.fallback] the response will be processed by LLM. If you need to return a default message when there is no recommended question in the knowledge base, use the [prompt.fallback_not_found_relevant_contents] configuration in the .toml (project configuration).

It is also possible to add information to the prompt for subcategories and choose some optional llm parameters like temperature (defaults to 0.2) or model_name, see below for an example of a complete configuration:

[model]
temperature = 0.2
model_name = "gpt-3.5-turbo"

[prompt]
header = """You are a service operator called Avelino from XYZ, you are an expert in providing
qualified service to high-end customers. Be brief in your answers, without being long-winded
and objective in your responses. Never say that you are a model (AI), always answer as Avelino.
Be polite and friendly!"""

suggested = "Here is some possible content that could help the user in a better way."

fallback = "I'm sorry, I couldn't find a relevant answer for your question."

fallback_not_found_relevant_contents = "I'm sorry, I couldn't find a relevant answer for your question."

[prompt.subcategory.loyalty-program]

header = """The client is interested in the loyalty program, and needs to be responded to in a
salesy way; the loyalty program is our growth strategy."""

Look at the .env.sample file to see the environment variables needed to run the project.

There are other configurations not detailed in .env.sample, we recommend you to read our documentation that discusses configuration.

we assume you are familiar with Docker

cp .env.sample .env # edit the .env file, add the OPENAI token and the path to the .csv and .toml files
docker-compose up